In [45]:
import sys
sys.setrecursionlimit(10000)
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
In [3]:
import cPickle
import gzip
from breze.learn.data import one_hot
from breze.learn.base import cast_array_to_local_type
from breze.learn.utils import tile_raster_images
import climin.stops
import climin.initialize
from breze.learn import hvi
from breze.learn.hvi import HmcViModel
from breze.learn import hvi
from breze.learn.hvi.energies import (NormalGaussKinEnergyMixin, DiagGaussKinEnergyMixin)
from breze.learn.hvi.inversemodels import MlpGaussInvModelMixin
from matplotlib import pyplot as plt
from matplotlib import cm
import numpy as np
#import fasttsne
from IPython.html import widgets
%matplotlib inline
import theano
theano.config.compute_test_value = 'ignore'#'raise'
from theano import tensor as T
In [4]:
datafile = '../mnist.pkl.gz'
# Load data.
with gzip.open(datafile,'rb') as f:
train_set, val_set, test_set = cPickle.load(f)
X, Z = train_set
VX, VZ = val_set
TX, TZ = test_set
Z = one_hot(Z, 10)
VZ = one_hot(VZ, 10)
TZ = one_hot(TZ, 10)
X_no_bin = X
VX_no_bin = VX
TX_no_bin = TX
# binarize the MNIST data
np.random.seed(0)
X = np.random.binomial(1, np.tile(X, (5, 1))) * 1.0
VX = np.random.binomial(1, VX) * 1.0
TX = np.random.binomial(1, TX) * 1.0
image_dims = 28, 28
X, Z, VX, VZ, TX, TZ = [cast_array_to_local_type(i) for i in (X, Z, VX,VZ, TX, TZ)]
print X.shape
In [5]:
fig, ax = plt.subplots(figsize=(9, 9))
img = tile_raster_images(X[:64], image_dims, (8, 8), (1, 1))
ax.imshow(img, cmap=cm.binary)
Out[5]:
In [6]:
fast_dropout = False
if fast_dropout:
class MyHmcViModel(HmcViModel,
hvi.FastDropoutMlpBernoulliVisibleVAEMixin,
hvi.FastDropoutMlpGaussLatentVAEMixin,
DiagGaussKinEnergyMixin,
MlpGaussInvModelMixin):
pass
kwargs = {
'p_dropout_inpt': .1,
'p_dropout_hiddens': [.2, .2],
}
print 'yeah'
else:
class MyHmcViModel(HmcViModel,
hvi.MlpBernoulliVisibleVAEMixin,
hvi.MlpGaussLatentVAEMixin,
DiagGaussKinEnergyMixin,
MlpGaussInvModelMixin):
pass
kwargs = {}
batch_size = 200
#optimizer = 'rmsprop', {'step_rate': 1e-4, 'momentum': 0.95, 'decay': .95, 'offset': 1e-6}
#optimizer = 'adam', {'step_rate': .5, 'momentum': 0.9, 'decay': .95, 'offset': 1e-6}
optimizer = 'adam', {'step_rate': 0.001}
# This is the number of random variables NOT the size of
# the sufficient statistics for the random variables.
n_latents = 2
n_hidden = 200
m = MyHmcViModel(X.shape[1], n_latents,
[n_hidden, n_hidden], ['rectifier'] * 2,
[n_hidden], ['rectifier'] * 1,
[n_hidden], ['rectifier'] * 1,
n_hmc_steps=3, n_lf_steps=4,
n_z_samples=1,
optimizer=optimizer, batch_size=batch_size, allow_partial_velocity_update=False, perform_acceptance_step=False,
**kwargs)
climin.initialize.randomize_normal(m.parameters.data, 0.1, 1e-1)
m.parameters.__setitem__(m.hmc_sampler.step_size_param, 0.1)
#m.parameters.__setitem__(m.kin_energy.mlp.layers[-1].bias, 1)
In [7]:
old_best_params = None
#print m.score(TX)
print m.parameters.data.shape
In [8]:
FILENAME = 'hvi_gen2_recog1_aux1_late2_hid200_newbin_3hmc_04lf_1z.pkl'
# In[5]:
#old_best_params = None
f = open(FILENAME, 'rb')
np_array = cPickle.load(f)
old_best_params = cast_array_to_local_type(np_array)
f.close()
print old_best_params.shape
In [9]:
m.parameters.data = old_best_params.copy()
old_best_loss = m.score(VX)
print old_best_loss
print m.score(TX)
In [26]:
print m.estimate_nll(TX, 500)
In [10]:
print m.parameters.view(m.hmc_sampler.step_size_param) ** 2 + 1e-5
In [None]:
max_passes = 250
max_iter = max_passes * X.shape[0] / batch_size
n_report = X.shape[0] / batch_size
stop = climin.stops.AfterNIterations(max_iter)
pause = climin.stops.ModuloNIterations(n_report)
# theano.config.optimizer = 'fast_compile'
for i, info in enumerate(m.powerfit((X,), (VX,), stop, pause, eval_train_loss=False)):
print i, info['loss'], info['val_loss']
if i == 0 and old_best_params is not None:
if info['best_loss'] > old_best_loss:
info['best_loss'] = old_best_loss
info['best_pars'] = old_best_params
if info['best_loss'] == info['val_loss']:
np.savetxt('hvi_short_basic_test.csv', m.parameters.data, delimiter=',')
In [11]:
print m.score(VX_no_bin)
print m.score(TX_no_bin)
In [46]:
print 1/min(TX[TX>0])
print (np.random.binomial(1, min(TX[TX>0]), size=(1000,))*1.0).sum()
print (np.random.binomial(1, np.tile(TX, (5, 1)))*1.0).shape
In [12]:
f_z_init_sample = m.function(['inpt'], m.init_recog.sample())
f_z_sample = m.function(['inpt'], m.hmc_sampler.output)
f_gen = m.function([m.gen.inpt], m.gen.sample())
f_gen_rate = m.function([m.gen.inpt], m.gen.rate)
In [16]:
from theano import tensor as T
curr_pos = T.matrix('current_position')
curr_vel = T.matrix('current_velocity')
norm_noise = T.matrix('normal_noise')
unif_noise = T.vector('uniform_noise')
new_sampled_vel = m.hmc_sampler.kin_energy.sample(norm_noise)
updated_vel = m.hmc_sampler.partial_vel_constant * curr_vel + m.hmc_sampler.partial_vel_complement * new_sampled_vel
performed_hmc_steps = m.hmc_sampler.perform_hmc_steps(curr_pos, curr_vel)
hmc_step = m.hmc_sampler.hmc_step(curr_pos, curr_vel, np.float32(0), norm_noise, unif_noise)
f_perform_hmc_steps = m.function(['inpt', curr_pos, curr_vel],
T.concatenate([performed_hmc_steps[0], performed_hmc_steps[1]], axis=1))
f_hmc_step = m.function(['inpt', curr_pos, curr_vel, norm_noise, unif_noise],
T.concatenate([hmc_step[0], hmc_step[1]],axis=1), on_unused_input='warn')
f_kin_energy_sample_from_noise = m.function(['inpt', norm_noise], new_sampled_vel)
f_updated_vel_from_noise = m.function(['inpt', curr_vel, norm_noise], updated_vel)
In [27]:
fig, axs = plt.subplots(3, 3, figsize=(27, 27))
### Original data
O = (X_no_bin[:64])[:, :784].astype('float32')
img = tile_raster_images(O, image_dims, (8, 8), (1, 1))
axs[0, 0].imshow(img, cmap=cm.binary)
O2 = (X[:64])[:, :784].astype('float32')
img = tile_raster_images(O2, image_dims, (8, 8), (1, 1))
axs[1, 0].imshow(img, cmap=cm.binary)
### Reconstruction
#z_sample = f_z_sample((X[:64]))
z_init_sample = f_z_init_sample((X[:64]))
z_sample = f_perform_hmc_steps((X[:64]),
z_init_sample,
f_kin_energy_sample_from_noise((X[:64]),
np.random.normal(size=(64, m.n_latent)).astype('float32'))
)[-1, :64, :]
R = f_gen_rate(z_sample)[:, :784].astype('float32')
img = tile_raster_images(R, image_dims, (8, 8), (1, 1))
axs[0, 1].imshow(img, cmap=cm.binary)
Rinit = f_gen_rate(z_init_sample)[:, :784].astype('float32')
img = tile_raster_images(Rinit, image_dims, (8, 8), (1, 1))
axs[0, 2].imshow(img, cmap=cm.binary)
R2 = f_gen(z_sample)[:, :784].astype('float32')
img = tile_raster_images(R2, image_dims, (8, 8), (1, 1))
axs[1, 1].imshow(img, cmap=cm.binary)
Rinit2 = f_gen(z_init_sample)[:, :784].astype('float32')
img = tile_raster_images(Rinit2, image_dims, (8, 8), (1, 1))
axs[1, 2].imshow(img, cmap=cm.binary)
### Sampling
prior_sample = np.random.randn(64, m.n_latent).astype('float32')
S = f_gen_rate(prior_sample)[:, :784].astype('float32')
img = tile_raster_images(S, image_dims, (8, 8), (1, 1))
axs[2, 0].imshow(img, cmap=cm.binary)
S2 = f_gen(prior_sample)[:, :784].astype('float32')
img = tile_raster_images(S2, image_dims, (8, 8), (1, 1))
axs[2, 1].imshow(img, cmap=cm.binary)
#S3 = f_gen_rate(prior_sample)[:, :784].astype('float32')
img = tile_raster_images(S, image_dims, (8, 8), (1, 1))
axs[2, 2].imshow(img, cmap=cm.nipy_spectral)
Out[27]:
In [23]:
from scipy.stats import norm as normal_distribution
unit_interval_positions = np.linspace(0.025, 0.975, 20)
positions = normal_distribution.ppf(unit_interval_positions)
print unit_interval_positions
print positions
latent_array = np.zeros((400, 2))
latent_array[:, 1] = -np.repeat(positions, 20) # because images are filled top -> bottom, left -> right (row by row)
latent_array[:, 0] = np.tile(positions, 20)
fig, axs = plt.subplots(1, 1, figsize=(24, 24))
F = f_gen_rate(latent_array.astype('float32'))
img = tile_raster_images(F, image_dims, (20, 20), (1, 1))
#axs.imshow(img, cmap=cm.nipy_spectral)
axs.imshow(img, cmap=cm.binary)
Out[23]:
In [28]:
from theano import tensor as T
from theano import clone
f_z_init_mean = m.function(['inpt'], m.init_recog.mean)
f_z_init_var = m.function(['inpt'], m.init_recog.var)
f_v_init_var = m.function(['inpt'], m.kin_energy.var)
full_sample = m.hmc_sampler.sample_with_path()
f_full_sample = m.function(['inpt'], T.concatenate([full_sample[0], full_sample[1]], axis=1))
In [29]:
#from theano.printing import debugprint
#debugprint(f_full_sample.theano_func)
In [30]:
final_pos = T.matrix('final_pos')
final_vel = T.matrix('final_vel')
inpt_replacements = {m.final_vel_model_inpt['position']: final_pos,
m.final_vel_model_inpt['time']: T.cast(m.hmc_sampler.n_hmc_steps, dtype='float32')}
final_vel_model_var = clone(m.final_vel_model.var, replace=inpt_replacements)
final_vel_model_mean = clone(m.final_vel_model.mean, replace=inpt_replacements)
final_vel_model_nll = clone(m.final_vel_model.nll(final_vel).sum(-1), replace=inpt_replacements)
f_v_final_var = m.function(['inpt', final_pos], final_vel_model_var)
f_v_final_mean = m.function(['inpt', final_pos], final_vel_model_mean)
f_v_final_model_nll = m.function(['inpt', final_pos, final_vel], final_vel_model_nll)
In [31]:
L = f_z_sample(X[:50000, :])
L_init = f_z_init_sample(X[:50000, :])
In [32]:
X_index = 0 # 5
num_repeats = 1000
In [33]:
#m.parameters.__setitem__(m.hmc_sampler.step_size_param, 0.08)
#m.parameters.__setitem__(m.kin_energy.variance_parameter, -0.5 * np.ones_like(m.kin_energy.variance_parameter))
#print m.parameters.view(m.kin_energy.variance_parameter)
#print m.parameters.view(m.hmc_sampler.step_size_param)
print f_z_init_mean(np.array([X[X_index, :]]))
print f_z_init_var(np.array([X[X_index, :]]))
print f_v_init_var(np.array([X[X_index, :]]))
#print f_v_final_var(np.array(X[:2]))
#print f_v_final_mean(np.array(X[:2]))
In [34]:
repeated_X = np.tile(np.array([X[X_index, :]]), (num_repeats, 1)).astype('float32')
full_sample = f_full_sample(repeated_X)
z_samples = full_sample[:, :num_repeats, :]
v_samples = full_sample[:, num_repeats:, :]
In [35]:
print z_samples[m.n_hmc_steps, :, :].mean(axis=0)
print z_samples[m.n_hmc_steps, :, :].var(axis=0)
In [36]:
z_sample_final_mean = z_samples[m.n_hmc_steps, :, :].mean(axis=0)
z_sample_final_std = z_samples[m.n_hmc_steps, :, :].std(axis=0)
variation_start = z_sample_final_mean - 2*z_sample_final_std
variation_end = z_sample_final_mean + 2*z_sample_final_std
final_vel_model_mean_output = np.zeros((m.n_latent, num_repeats, m.n_latent))
final_vel_model_var_output = np.zeros((m.n_latent, num_repeats, m.n_latent))
for variation_dim in range(m.n_latent):
z_variation = np.linspace(variation_start[variation_dim], variation_end[variation_dim], num_repeats)
sample_array = np.tile(z_sample_final_mean, (num_repeats, 1))
sample_array[:, variation_dim] = z_variation
final_vel_model_mean_output[variation_dim] = f_v_final_mean(repeated_X, sample_array)
final_vel_model_var_output[variation_dim] = f_v_final_var(repeated_X, sample_array)
In [37]:
dim1 = 0
dim2 = 1
In [38]:
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
axs[0].scatter(L[:, dim1], L[:, dim2], c=Z[:].argmax(1), lw=0, s=5, alpha=.2)
axs[1].scatter(L_init[:, dim1], L_init[:, dim2], c=Z[:].argmax(1), lw=0, s=5, alpha=.2)
import matplotlib as mpl
cax = fig.add_axes([0.95, 0.2, 0.02, 0.6])
#norm = mpl.colors.Normalize(vmin=0, vmax=9)
#cbar = mpl.colorbar.ColorbarBase(cax, norm=norm, spacing='proportional')
cax.scatter(np.repeat(0, 10), np.arange(10), c=np.arange(10), lw=0, s=300)
cax.set_xlim(-0.1, 0.1)
cax.set_ylim(-0.5, 9.5)
plt.yticks(np.arange(10))
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
cax.tick_params(axis='y', colors='white')
for tick in cax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
tick.label.set_color('black')
cax.spines['bottom'].set_color('white')
cax.spines['top'].set_color('white')
cax.spines['right'].set_color('white')
cax.spines['left'].set_color('white')
axs[0].set_title('After HMC steps')
axs[1].set_title('Initial recognition model')
axs[0].set_xlim(-3, 3)
axs[0].set_ylim(-3, 3)
axs[1].set_xlim(-3, 3)
axs[1].set_ylim(-3, 3)
Out[38]:
In [41]:
fig, axs = plt.subplots(4, 5, figsize=(20, 16))
colors = cm.jet(np.linspace(0, 1, 10))
for i in range(5):
axs[0, i].scatter(L_init[Z[:].argmax(1) == i, dim1], L_init[Z[:].argmax(1) == i, dim2], c=colors[i], lw=0, s=5, alpha=.2)
axs[1, i].scatter(L[Z[:].argmax(1) == i, dim1], L[Z[:].argmax(1) == i, dim2], c=colors[i], lw=0, s=5, alpha=.2)
axs[0, i].set_title(str(i) + ' before HMC')
axs[1, i].set_title(str(i) + ' after HMC')
axs[2, i].scatter(L_init[Z[:].argmax(1) == (5+i), dim1], L_init[Z[:].argmax(1) == (5+i), dim2], c=colors[5+i], lw=0, s=5, alpha=.2)
axs[3, i].scatter(L[Z[:].argmax(1) == (5+i), dim1], L[Z[:].argmax(1) == (5+i), dim2], c=colors[5+i], lw=0, s=5, alpha=.2)
axs[2, i].set_title(str(5+i) + ' before HMC')
axs[3, i].set_title(str(5+i) + ' after HMC')
for j in range(4):
axs[j, i].set_xlim(-3, 3)
axs[j, i].set_ylim(-3, 3)
In [42]:
resolution = 200
lower_dim1_limit = z_sample_final_mean[dim1] - 0.5
upper_dim1_limit = z_sample_final_mean[dim1] + 0.5
lower_dim2_limit = z_sample_final_mean[dim2] - 0.5
upper_dim2_limit = z_sample_final_mean[dim2] + 0.5
pos = T.matrix()
pot_en = m.function([pos, m.inpt], m.hmc_sampler.eval_pot_energy(pos))
pot_energy_matrix = np.zeros((resolution, resolution), dtype='float32')
x = cast_array_to_local_type(np.linspace(lower_dim1_limit, upper_dim1_limit, resolution))
y = cast_array_to_local_type(np.linspace(lower_dim2_limit, upper_dim2_limit, resolution))
for i in range(resolution):
for j in range(resolution):
#pos_array = f_z_init_mean(np.array([X[X_index, :]]))
pos_array = np.array([z_samples[m.n_hmc_steps, :, :].mean(axis=0)])
pos_array[0, dim1] = x[i]
pos_array[0, dim2] = y[j]
pot_energy_matrix[j, i] = pot_en(pos_array, np.array([X[X_index, :]]))
fig, ax = plt.subplots(1, 1, figsize=(9, 9))
CS = ax.contour(x, y, pot_energy_matrix)
plt.clabel(CS, inline=1, fmt='%r', fontsize=10)
plt.show()
In [47]:
fig, axs = plt.subplots(4, 2, figsize=(18, 36))
#contour_levels = (198, 200, 202, 204, 206, 208, 210)
#contour_levels = (130, 140, 150, 160, 180, 200, 240, 280)
#contour_levels = (180, 190, 200, 220, 240, 260, 280, 300, 320)
#contour_levels = (400, 402, 404, 406, 408, 410, 412, 416, 420)
#contour_levels = (106, 108, 110, 112, 114, 116, 118, 120, 124, 128)
contour_levels = (160, 165, 170, 175, 180, 185, 190, 200, 210, 215, 217, 220, 230, 240)
#CS0 = axs[0, 0].contourf(x, y, pot_energy_matrix, np.linspace(155, 240, 500))
CS0 = axs[0, 0].contour(x, y, pot_energy_matrix, contour_levels)
CS1 = axs[1, 0].contour(x, y, pot_energy_matrix, contour_levels)
CS2 = axs[2, 0].contour(x, y, pot_energy_matrix, contour_levels)
CS3 = axs[3, 0].contour(x, y, pot_energy_matrix, contour_levels)
def colour_for_z_samples(samples):
mean = samples.mean(axis=0)
mean1 = mean[dim1]
mean2 = mean[dim2]
colour = np.zeros_like(samples[:, 0])
colour[np.logical_and(samples[:, dim1] < mean1, samples[:, dim2] < mean2)] = 0
colour[np.logical_and(samples[:, dim1] < mean1, samples[:, dim2] >= mean2)] = 2
colour[np.logical_and(samples[:, dim1] >= mean1, samples[:, dim2] < mean2)] = 4
colour[np.logical_and(samples[:, dim1] >= mean1, samples[:, dim2] >= mean2)] = 7
colour[((samples[:, dim1] - mean1) ** 2 + (samples[:, dim2] - mean2) ** 2) < 1e-3] = 9
return colour.astype('int32')
colour = colour_for_z_samples(z_samples[m.n_hmc_steps,:,:])
print v_samples[m.n_hmc_steps, colour == 0, :].mean(axis=0)
print v_samples[m.n_hmc_steps, colour == 2, :].mean(axis=0)
print v_samples[m.n_hmc_steps, colour == 4, :].mean(axis=0)
print v_samples[m.n_hmc_steps, colour == 7, :].mean(axis=0)
print v_samples[m.n_hmc_steps, colour == 9, :].mean(axis=0)
print v_samples[m.n_hmc_steps, colour == 0, :].var(axis=0)
print v_samples[m.n_hmc_steps, colour == 2, :].var(axis=0)
print v_samples[m.n_hmc_steps, colour == 4, :].var(axis=0)
print v_samples[m.n_hmc_steps, colour == 7, :].var(axis=0)
print v_samples[m.n_hmc_steps, colour == 9, :].var(axis=0)
axs[0, 0].scatter(z_samples[0,:,dim1], z_samples[0,:,dim2], c=colors[colour_for_z_samples(z_samples[0,:,:])], s=20, alpha=.3, lw=0)
axs[1, 0].scatter(z_samples[1,:,dim1], z_samples[1,:,dim2], c=colors[colour_for_z_samples(z_samples[1,:,:])], s=20, alpha=.3, lw=0)
axs[2, 0].scatter(z_samples[2,:,dim1], z_samples[2,:,dim2], c=colors[colour_for_z_samples(z_samples[2,:,:])], s=20, alpha=.3, lw=0)
axs[3, 0].scatter(z_samples[3,:,dim1], z_samples[3,:,dim2], c=colors[colour_for_z_samples(z_samples[3,:,:])], s=20, alpha=.3, lw=0)
axs[0, 0].set_title('Initial recognition model')
axs[1, 0].set_title('After 1 HMC step')
axs[2, 0].set_title('After 2 HMC step')
axs[3, 0].set_title('After 3 HMC step')
axs[0, 1].scatter(v_samples[0,:,dim1], v_samples[0,:,dim2], c=colors[colour_for_z_samples(z_samples[0,:,:])], s=20, alpha=.3, lw=0)
axs[1, 1].scatter(v_samples[1,:,dim1], v_samples[1,:,dim2], c=colors[colour_for_z_samples(z_samples[1,:,:])], s=20, alpha=.3, lw=0)
axs[2, 1].scatter(v_samples[2,:,dim1], v_samples[2,:,dim2], c=colors[colour_for_z_samples(z_samples[2,:,:])], s=20, alpha=.3, lw=0)
axs[3, 1].scatter(v_samples[3,:,dim1], v_samples[3,:,dim2], c=colors[colour_for_z_samples(z_samples[3,:,:])], s=20, alpha=.3, lw=0)
plt.clabel(CS0, inline=1, fmt='%r', fontsize=10)
plt.clabel(CS1, inline=1, fmt='%r', fontsize=10)
plt.clabel(CS2, inline=1, fmt='%r', fontsize=10)
plt.clabel(CS3, inline=1, fmt='%r', fontsize=10)
print pot_energy_matrix.min()
print pot_energy_matrix.max()
axs[0, 0].scatter(f_z_init_mean(np.array([X[X_index,:]]))[0, dim1], f_z_init_mean(np.array([X[X_index,:]]))[0, dim2], c='black', s=20)
#axs[0, 1].set_xlim(-3, 3)
#axs[0, 1].set_ylim(-3, 3)
#axs[1, 1].set_xlim(-3, 3)
#axs[1, 1].set_ylim(-3, 3)
#axs[2, 1].set_xlim(-3, 3)
#axs[2, 1].set_ylim(-3, 3)
#axs[3, 1].set_xlim(-3, 3)
#axs[3, 1].set_ylim(-3, 3)
plt.show()
In [44]:
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
axs[0].scatter(final_vel_model_mean_output[:, :, dim1],
final_vel_model_mean_output[:, :, dim2],
c=np.transpose(np.tile(np.linspace(0,m.n_latent-1,m.n_latent), (num_repeats, 1))),
lw=0, s=5)
axs[1].scatter(final_vel_model_var_output[:, :, dim1],
final_vel_model_var_output[:, :, dim2],
c=np.transpose(np.tile(np.linspace(0,m.n_latent-1,m.n_latent), (num_repeats, 1))),
lw=0, s=5)
plt.show()
In [121]:
final_vel_mean = f_v_final_mean(repeated_X, z_samples[3, :, :])
final_vel_var = f_v_final_var(repeated_X, z_samples[3, :, :])
final_vel_nll = f_v_final_model_nll(repeated_X, z_samples[3, :, :], v_samples[3, :, :])
In [122]:
fig, axs = plt.subplots(4, 2, figsize=(18, 36))
# TODO: Analysis of how final_vel_mean and final_vel_var depend on z (since they all share the same x)
print z_samples[3, :, :].mean(axis=0)
print z_samples[3, :, :].var(axis=0)
print v_samples[3, :, :].mean(axis=0)
print v_samples[3, :, :].var(axis=0)
print f_v_init_var(np.array([X[X_index, :]]))
print final_vel_nll.mean()
plt.boxplot(final_vel_nll, whis=1)
plt.show()
In [11]:
centers = np.zeros((10,n_latents))
stddevs = np.zeros((10,n_latents))
centers_init = np.zeros((10,n_latents))
stddevs_init = np.zeros((10,n_latents))
for i in range(10):
Li = f_z_sample(X[Z.argmax(1) == i])
centers[i] = Li.mean(axis=0)
stddevs[i] = np.std(Li, axis=0)
Li_init = f_z_init_sample(X[Z.argmax(1) == i])
centers_init[i] = Li_init.mean(axis=0)
stddevs_init[i] = np.std(Li_init, axis=0)
In [33]:
fig, axs = plt.subplots(1, 2, figsize=(18, 9))
axs[0].scatter(centers[:, dim1], centers[:, dim2], c=range(10), s=50)
axs[0].scatter(centers_init[:, dim1], centers_init[:, dim2], c=range(10), s=50, marker=u's')
axs[1].scatter(centers[:, dim1], centers[:, dim2], c=range(10), s=50)
axs[1].scatter(centers[:, dim1] + stddevs[:, dim1], centers[:, dim2], c=range(10), s=50, marker=u'>')
axs[1].scatter(centers[:, dim1] - stddevs[:, dim1], centers[:, dim2], c=range(10), s=50, marker=u'<')
axs[1].scatter(centers[:, dim1], centers[:, dim2] + stddevs[:, dim2], c=range(10), s=50, marker=u'^')
axs[1].scatter(centers[:, dim1], centers[:, dim2] - stddevs[:, dim2], c=range(10), s=50, marker=u'v')
#axs[0].set_xlim(-1.2, 1.2)
#axs[0].set_ylim(-1.2, 1.2)
#axs[1].set_xlim(-1.2, 1.2)
#axs[1].set_ylim(-1.2, 1.2)
print (centers[:, dim1] - centers_init[:, dim1])
print (centers[:, dim2] - centers_init[:, dim2])
print (stddevs[:, dim1] - stddevs_init[:, dim1])
print (stddevs[:, dim2] - stddevs_init[:, dim2])
In [None]: